import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np


# Dataset
fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images,
                               test_labels) = fashion_mnist.load_data()


class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']


train_images = train_images / 255.0

test_images = test_images / 255.0


# Model

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

# Train
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(
                  from_logits=True),
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)
